With human society becoming increasingly computerised, the use of biometrics to automatically establish the identity of an individual is of great interest in a wide variety of applications. Facial appearance is an appealing biometric, on account of its relatively non-intrusive nature. As such, automated face recognition systems have been the subject of much research in recent years. This dissertation describes the development of a fully automatic face recognition system, and provides an analysis of its performance under various di erent operating conditions, in comparison with results published in prior literature. In addition to giving a detailed description of the mathematical underpinnings of the techniques used by the system, we discuss the practical considerations involved in implementing the described techniques. The system presented here uses the eigenface approach to representing facial features. A number of di erent recognition techniques have been implemented and evaluated. These include a number of variants of the original eigenface technique proposed by Turk and Pentland, as well as a related technique based on the probabilistic approach of Moghaddam et al. Due to the wide range of datasets used to evaluate face recognition systems in the literature, it is di cult to reliably compare the performance of di erent systems. The system described here has been tested with datasets encompassing a wide range of di erent conditions, allowing us to draw conclusions about how the characteristics of the test data a ect the results that are obtained. The performance of this system is comparable to other eigenface-based systems documented in the literature, achieving success rates in the region of 85% for large datasets under controlled conditions. However, performance was observed to degrade signi cantly when testing with more free-form images; in particular, the e ects of ageing on facial appearance were noted to cause problems for the system. This suggests that the matter of ageing is still a fruitful direction for further research. / Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2012.